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class="external-links-container"><ul class="profile-links new-profile js-UserInfo-social"><li class="profile-profiles js-social-profiles-container"><i class="fa fa-spin fa-spinner"></i></li></ul></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Hiroaki Wagatsuma</h3></div><div class="js-work-strip profile--work_container" data-work-id="7871606"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/7871606/Problems_of_temporal_granularity_in_robot_control_Levels_of_adaptation_and_a_necessity_of_self_confidence"><img alt="Research paper thumbnail of Problems of temporal granularity in robot control: Levels of adaptation and a necessity of self-confidence" class="work-thumbnail" src="https://attachments.academia-assets.com/48307122/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/7871606/Problems_of_temporal_granularity_in_robot_control_Levels_of_adaptation_and_a_necessity_of_self_confidence">Problems of temporal granularity in robot control: Levels of adaptation and a necessity of self-confidence</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The granularity of "action" within a system is highly depending on the internal representation fo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The granularity of "action" within a system is highly depending on the internal representation for the task, or intention of what to do if it is a biological system. In the same time, there are several levels of adaptation when the system tries to complete a mission. The problem of choosing the right level of action representation is essential for robot controls a s well as in learning paradigms. Both tend to use lowgranularity and transfer the processed information to upper levels constructively. However the system never guarantees the completion time of the mission if the system is composed of stiff functional blocks with a specific temporal granularity at the bottom level. However, we biological system have an ability to manage the global time for scheduling and reorganization of tasks to finish by the deadline. Brain-inspired robotics allows us to investigate a distributed parallel information system, the brain, with the ability of time management as a real time control system of the physical body through flexible planning of necessary actions by interacting with the real environment. It is an extension of subsumption approaches that fixed a set of behaviors as the basic unit of action in the viewpoint of temporal property. By focusing on the temporal granularity a s a consequence of coordination among multiple levels, a selfconfident robot control may arise from a coupling between top-down or purpose-oriented decomposition of the purpose to primitive functions with flexible time windows and bottom-up of sensori-motor reactions in dynamic environments.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4c162aa7f0cbba1742c727aa4932fcdf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":48307122,"asset_id":7871606,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/48307122/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871606"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871606"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871606; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871602"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/7871602/Cognitive_robotics_new_insights_into_robot_and_human_intelligence_by_reverse_engineering_brain_functions_From_the_Guest_Editors"><img alt="Research paper thumbnail of Cognitive robotics: new insights into robot and human intelligence by reverse engineering brain functions [From the Guest Editors" class="work-thumbnail" src="https://attachments.academia-assets.com/48307311/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/7871602/Cognitive_robotics_new_insights_into_robot_and_human_intelligence_by_reverse_engineering_brain_functions_From_the_Guest_Editors">Cognitive robotics: new insights into robot and human intelligence by reverse engineering brain functions [From the Guest Editors</a></div><div class="wp-workCard_item"><span>IEEE Robotics & Automation Magazine</span><span>, 2009</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4267f2a39e125944ed72926af38c2489" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":48307311,"asset_id":7871602,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/48307311/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871602"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871602"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871602; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871602]").text(description); $(".js-view-count[data-work-id=7871602]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871602; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871602']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871600"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/7871600/Hybrid_Design_Principles_and_Time_Constants_in_the_Construction_of_Brain_Based_Robotics_A_Real_Time_Simulator_of_Oscillatory_Neural_Networks_Interacting_with_the_Real_Environment_via_Robotic_Devices"><img alt="Research paper thumbnail of Hybrid Design Principles and Time Constants in the Construction of Brain-Based Robotics: A Real-Time Simulator of Oscillatory Neural Networks Interacting with the Real Environment via Robotic Devices" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/7871600/Hybrid_Design_Principles_and_Time_Constants_in_the_Construction_of_Brain_Based_Robotics_A_Real_Time_Simulator_of_Oscillatory_Neural_Networks_Interacting_with_the_Real_Environment_via_Robotic_Devices">Hybrid Design Principles and Time Constants in the Construction of Brain-Based Robotics: A Real-Time Simulator of Oscillatory Neural Networks Interacting with the Real Environment via Robotic Devices</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One of most important concepts in robotics and artificial intelligence is the embodied approach, ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">One of most important concepts in robotics and artificial intelligence is the embodied approach, focusing on the importance of having a body that functionally connects to the external world. This setup suggests that the intelligence develops through sensorimotor skills and through situations that would actually be confronted in the environment. We support this concept and propose to further extend it to embodiment in the time domain. Nervous systems have variable processing times. The different time courses proceed in the nervous system in parallel, and individual circuits independently and cooperatively work under the constraints of temporal properties. We here propose an experimental platform of oscillatory neural networks having real-time communication with the environment through the robot’s body. The synchronization mechanism of oscillations in neural activities have the advantage of synthetic controls known in motor coordination, but we extend this to circuits for cognitive functions like episodic memory formation and decision making of the robotic behavior by using the theta phase coding mechanism. A slow oscillation, like the theta rhythm, enables behavioral temporal sequences to be compressed in sequential firings during each oscillation cycle, and this helps to represent cognitive information in episodes composed of past-present-future structures. The temporal structure is crucial for recognition of the current context and adaptability in dynamic environments, and it smoothly controls sensorimotor local circuits with faster time scales. This work represents a tiny step towards constructing the brain by focusing on the temporal structure, yet this approach may elucidate the new nature of the brain-based intelligence.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871600"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871600"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871600; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871600]").text(description); $(".js-view-count[data-work-id=7871600]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871600; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871600']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=7871600]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7871600,"title":"Hybrid Design Principles and Time Constants in the Construction of Brain-Based Robotics: A Real-Time Simulator of Oscillatory Neural Networks Interacting with the Real Environment via Robotic Devices","internal_url":"https://www.academia.edu/7871600/Hybrid_Design_Principles_and_Time_Constants_in_the_Construction_of_Brain_Based_Robotics_A_Real_Time_Simulator_of_Oscillatory_Neural_Networks_Interacting_with_the_Real_Environment_via_Robotic_Devices","owner_id":14698680,"coauthors_can_edit":true,"owner":{"id":14698680,"first_name":"Hiroaki","middle_initials":null,"last_name":"Wagatsuma","page_name":"HiroakiWagatsuma","domain_name":"facebook","created_at":"2014-08-04T13:16:19.153-07:00","display_name":"Hiroaki Wagatsuma","url":"https://facebook.academia.edu/HiroakiWagatsuma"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871599"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/7871599/Cultivated_Microorganisms_Control_a_Real_Robot_A_Model_of_Dynamical_Coupling_between_Internal_Growth_and_Robot_Movement"><img alt="Research paper thumbnail of Cultivated Microorganisms Control a Real Robot: A Model of Dynamical Coupling between Internal Growth and Robot Movement" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/7871599/Cultivated_Microorganisms_Control_a_Real_Robot_A_Model_of_Dynamical_Coupling_between_Internal_Growth_and_Robot_Movement">Cultivated Microorganisms Control a Real Robot: A Model of Dynamical Coupling between Internal Growth and Robot Movement</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In biological systems, internal microorganisms adaptively survive but often serve necessary funct...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In biological systems, internal microorganisms adaptively survive but often serve necessary functions to the host, such as energy production by mitochondria as symbiosis. On the other hand, malignant germs have a parasitic relationship with the host and provide no benefit. A significant property that distinguishes healthy symbioses and malignant parasites is reproduction speed, or pace. For example, the rapid reproduction of influenza viruses destructs the host system, resulting in death. This study explored the necessary temporal property to establish a healthy relationship with the host under conditions where internal organisms have individual life spans. We propose a simple model of microorganisms, which are distributed spatially as colonial organizations undergoing temporal evolution and hypothesize that a self-consistent rhythm generated in collective behavior that is functionally coupled with the temporal global property of the host system is critical. To investigate the real-time coordination capability, an experimental framework with a mobile robot moving in the real world was used. As the on-line system, the microorganism model controls this robot. In this model, microorganisms expanded spatially and had colonial and power law distributions through time evolution. The neighboring distances, which are crucial for reproduction speed and are globally modulated by the size of the whole living area, are plastically changed to exhibit a rhythmic modulation. In the real-environmental experiment, the robot’s navigation was successfully demonstrated by producing a temporal adaptability of microorganisms with the living area reshaped according to the current sensory information of the mobile robot. This is a first step of the microorganism-based framework to investigate the real-time coordination mechanism between internal and external timescales. The result may further groundbreaking research of bio-morphological robots.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871599"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871599"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871599; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871599]").text(description); $(".js-view-count[data-work-id=7871599]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871599; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871599']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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</script> <div class="js-work-strip profile--work_container" data-work-id="7871594"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/7871594/Computational_study_of_memory_formation_through_dynamical_interplays_in_the_cortico_hippocampal_system"><img alt="Research paper thumbnail of Computational study of memory formation through dynamical interplays in the cortico-hippocampal system" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/7871594/Computational_study_of_memory_formation_through_dynamical_interplays_in_the_cortico_hippocampal_system">Computational study of memory formation through dynamical interplays in the cortico-hippocampal system</a></div><div class="wp-workCard_item"><span>Neuroscience Research</span><span>, 2009</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871594"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871594"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871594; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871594]").text(description); $(".js-view-count[data-work-id=7871594]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871594; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871594']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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</script> <div class="js-work-strip profile--work_container" data-work-id="7871593"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/7871593/A_unified_view_of_theta_phase_coding_in_the_entorhinal_hippocampal_system"><img alt="Research paper thumbnail of A unified view of theta-phase coding in the entorhinal–hippocampal system" class="work-thumbnail" src="https://attachments.academia-assets.com/48307149/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/7871593/A_unified_view_of_theta_phase_coding_in_the_entorhinal_hippocampal_system">A unified view of theta-phase coding in the entorhinal–hippocampal system</a></div><div class="wp-workCard_item"><span>Current Opinion in Neurobiology</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The discovery of theta-rhythm-dependent firing of rodent hippocampal neurons highlighted the func...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The discovery of theta-rhythm-dependent firing of rodent hippocampal neurons highlighted the functional significance of temporal encoding in hippocampal memory. However, earlier theoretical studies on this topic seem divergent and experimental implications are invariably complicated. To obtain a unified understanding of neural dynamics in the hippocampal memory, we here review recent developments in computational models and experimental discoveries on the 'theta-phase precession' of hippocampal place cells and entorhinal grid cells. We identify a theoretical hypothesis that is well supported by experimental facts; this model reveals a significant contribution of theta-phase coding to the on-line real-time operation of episodic events, through highly parallel representation of spatiotemporal information.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e8ce3af864477631e2024c99c55efe60" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":48307149,"asset_id":7871593,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/48307149/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871593"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871593"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871593; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e8ce3af864477631e2024c99c55efe60" } } $('.js-work-strip[data-work-id=7871593]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7871593,"title":"A unified view of theta-phase coding in the entorhinal–hippocampal system","internal_url":"https://www.academia.edu/7871593/A_unified_view_of_theta_phase_coding_in_the_entorhinal_hippocampal_system","owner_id":14698680,"coauthors_can_edit":true,"owner":{"id":14698680,"first_name":"Hiroaki","middle_initials":null,"last_name":"Wagatsuma","page_name":"HiroakiWagatsuma","domain_name":"facebook","created_at":"2014-08-04T13:16:19.153-07:00","display_name":"Hiroaki Wagatsuma","url":"https://facebook.academia.edu/HiroakiWagatsuma"},"attachments":[{"id":48307149,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/48307149/thumbnails/1.jpg","file_name":"A_unified_view_of_theta-phase_coding_in_20160825-14563-1dv6x2f.pdf","download_url":"https://www.academia.edu/attachments/48307149/download_file","bulk_download_file_name":"A_unified_view_of_theta_phase_coding_in.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/48307149/A_unified_view_of_theta-phase_coding_in_20160825-14563-1dv6x2f-libre.pdf?1472132437=\u0026response-content-disposition=attachment%3B+filename%3DA_unified_view_of_theta_phase_coding_in.pdf\u0026Expires=1740000684\u0026Signature=Ct7klf0eTjLL6Sy4C84m9YqO5PFCcBqWLimwUlG1JA88LlQki9iVfG-xR4hBThebRIZpwrQLjt2-XUbMrfsXbdZ~II5~yK8160brA92wzZukNj3Ki85T0CG5wqBDY04KLL6XUGjMr6NKrwTRQlCSTT3Z4iBfyVdC-6UW8fsNnv6tmjW6Atkur-sqHCgeVpiovzjwgKmicmGQe9JJVMADaInodRe5dOpCNGY6O9LTMGSLiFnxPrPzM-3Pn7PJFpRFFYyr3gInNXjxmThnXxYI-2jk7hxwE7TwLkCjBqTelQmz7R73sMexl0F6V4Ph8xh1WbXhkDjOKcPtZRqTrxybgg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871592"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/7871592/Dynamic_Brain_Platform"><img alt="Research paper thumbnail of Dynamic Brain Platform" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/7871592/Dynamic_Brain_Platform">Dynamic Brain Platform</a></div><div class="wp-workCard_item"><span>Neuroscience Research</span><span>, 2009</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871592"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871592"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871592; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871592]").text(description); $(".js-view-count[data-work-id=7871592]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871592; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871592']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=7871592]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7871592,"title":"Dynamic Brain Platform","internal_url":"https://www.academia.edu/7871592/Dynamic_Brain_Platform","owner_id":14698680,"coauthors_can_edit":true,"owner":{"id":14698680,"first_name":"Hiroaki","middle_initials":null,"last_name":"Wagatsuma","page_name":"HiroakiWagatsuma","domain_name":"facebook","created_at":"2014-08-04T13:16:19.153-07:00","display_name":"Hiroaki Wagatsuma","url":"https://facebook.academia.edu/HiroakiWagatsuma"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871591"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/7871591/Synchronized_Rhythmic_Signals_Effectively_Influence_Ongoing_Cortical_Activity_for_Decision_Making_A_Study_of_the_Biological_Plausible_Neural_Network_Model"><img alt="Research paper thumbnail of Synchronized Rhythmic Signals Effectively Influence Ongoing Cortical Activity for Decision-Making: A Study of the Biological Plausible Neural Network Model" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/7871591/Synchronized_Rhythmic_Signals_Effectively_Influence_Ongoing_Cortical_Activity_for_Decision_Making_A_Study_of_the_Biological_Plausible_Neural_Network_Model">Synchronized Rhythmic Signals Effectively Influence Ongoing Cortical Activity for Decision-Making: A Study of the Biological Plausible Neural Network Model</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The brain is capable of parallel processing of different types of information in different brain ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The brain is capable of parallel processing of different types of information in different brain regions. However, for higher cognitive functions such as decision-making, such regionally distributed information must interact together in the proper timing. The prefrontal cortex, the region which carries out decision-making, needs to be modulated by external signals to represent the current behavioral context in the hippocampus (HP). The question remains as to how the firing activity of the cortical neural network can be modulated by external signals in corporation with the ongoing activity. We hypothesized that rhythmic signals that attempt to synchronize the cortical ongoing activity minimize the disturbance and effectively enhance the activities of selective neurons. We investigated the level of the modulation by using a mutually connected neural network that consists of a neuron model with excitatory and refractory periods. The results demonstrated that cortical ongoing activities are weakly modulated by random external signals, while synchronized rhythmic signals, given as the pseudo HP signals, selectively enhance cortical activities. This suggests that the cortical ongoing activity is effectively influenced by the synchronized signals, which carry information in the proper timing of excitation. The investigation of neural synchronization dynamics is important to understanding how the brain realizes parallel processing in different sub-regions and to update immediately the internal representation even if the previous internal processing is ongoing.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871591"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871591"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871591; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871591]").text(description); $(".js-view-count[data-work-id=7871591]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871591; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871591']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=7871591]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7871591,"title":"Synchronized Rhythmic Signals Effectively Influence Ongoing Cortical Activity for Decision-Making: A Study of the Biological Plausible Neural Network Model","internal_url":"https://www.academia.edu/7871591/Synchronized_Rhythmic_Signals_Effectively_Influence_Ongoing_Cortical_Activity_for_Decision_Making_A_Study_of_the_Biological_Plausible_Neural_Network_Model","owner_id":14698680,"coauthors_can_edit":true,"owner":{"id":14698680,"first_name":"Hiroaki","middle_initials":null,"last_name":"Wagatsuma","page_name":"HiroakiWagatsuma","domain_name":"facebook","created_at":"2014-08-04T13:16:19.153-07:00","display_name":"Hiroaki Wagatsuma","url":"https://facebook.academia.edu/HiroakiWagatsuma"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871590"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/7871590/A_neural_network_model_self_organizing_a_cognitive_map_using_theta_phase_precession"><img alt="Research paper thumbnail of A neural network model self-organizing a cognitive map using theta phase precession" class="work-thumbnail" src="https://attachments.academia-assets.com/48307109/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/7871590/A_neural_network_model_self_organizing_a_cognitive_map_using_theta_phase_precession">A neural network model self-organizing a cognitive map using theta phase precession</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The cognitive map is thought to consist of place cells in the hippocampus. We hypothesize that “t...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The cognitive map is thought to consist of place cells in the hippocampus. We hypothesize that “theta phase precession”, a characteristic relation between the firing of the place cell and local field theta rhythm, contributes to the generation of the cognitive map as a network of place cells. We propose a network model of the hippocampus based on the hypothesis. In our model, the entorhinal cortex (EC) receives sensory-inputs of local views. The dynamic pattern of theta phase precession is generated by mutual synchronization of neural oscillators in the EC. In CA3, neural units receive inputs from the EC and the cognitive map is self-organized in the recurrent neural network. In the computer experiments, inheritance of theta phase precession in CA3 results in a kind of phase wave propagating along a 2-D plane. The 2-D plane is define as the desired array of place cells in accordance with their place fields. The wave propagation deriving from theta phase precession globally controls the synaptic modification among place cells so that their network is self-organized as the cognitive map</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f9c62de428f50ae6f2fb5d202a97a59e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":48307109,"asset_id":7871590,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/48307109/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871590"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871590"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871590; 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An information retrieval agent for classification of Web pages based on genre features is used. In developing the agent to adapt to users' search preferences, a neuro-cognitive model of human episodic memory is employed. Our studies show that neuro-realistic models, capable of abstraction of meaningful fragments of knowledge, rather than snapshots of the retrieved Web pages, are closer to the human way of interacting with the Web and can be used for optimization of agent performance.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c83c9fdbee995c6294e5fa663b112924" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":34361809,"asset_id":7871589,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/34361809/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871589"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871589"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871589; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871588"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/7871588/Neural_dynamics_of_the_cognitive_map_in_the_hippocampus"><img alt="Research paper thumbnail of Neural dynamics of the cognitive map in the hippocampus" class="work-thumbnail" src="https://attachments.academia-assets.com/48307113/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/7871588/Neural_dynamics_of_the_cognitive_map_in_the_hippocampus">Neural dynamics of the cognitive map in the hippocampus</a></div><div class="wp-workCard_item"><span>Cognitive Neurodynamics</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The rodent hippocampus has been thought to represent the spatial environment as a cognitive map. ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The rodent hippocampus has been thought to represent the spatial environment as a cognitive map. In the classical theory, the cognitive map has been explained as a consequence of the fact that different spatial regions are assigned to different cell populations in the framework of rate coding. Recently, the relation between place cell firing and local field oscillation theta in terms of theta phase precession was experimentally discovered and suggested as a temporal coding mechanism leading to memory formation of behavioral sequences accompanied with asymmetric Hebbian plasticity. The cognitive map theory is apparently outside of the sequence memory view. Therefore, theoretical analysis is necessary to consider the biological neural dynamics for the sequence encoding of the memory of behavioral sequences, providing the cognitive map formation. In this article, we summarize the theoretical neural dynamics of the real-time sequence encoding by theta phase precession, called theta phase coding, and review a series of theoretical models with the theta phase coding that we previously reported. With respect to memory encoding functions, instantaneous memory formation of one-time experience was first demonstrated, and then the ability of integration of memories of behavioral sequences into a network of the cognitive map was shown. In terms of memory retrieval functions, theta phase coding enables the hippocampus to represent the spatial location in the current behavioral context even with ambiguous sensory input when multiple sequences were coded. Finally, for utilization, retrieved temporal sequences in the hippocampus can be available for action selection, through the process of reverting theta rhythm-dependent activities to information in the behavioral time scale. This theoretical approach allows us to investigate how the behavioral sequences are encoded, updated, retrieved and used in the hippocampus, as the real-time interaction with the external environment. It may indeed be the bridge to the episodic memory function in human hippocampus.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cac93cf507365276718b703057aabf16" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":48307113,"asset_id":7871588,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/48307113/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871588"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871588"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871588; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871587"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/7871587/Disambiguation_in_spatial_navigation_with_theta_phase_coding"><img alt="Research paper thumbnail of Disambiguation in spatial navigation with theta phase coding" class="work-thumbnail" src="https://attachments.academia-assets.com/48307137/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/7871587/Disambiguation_in_spatial_navigation_with_theta_phase_coding">Disambiguation in spatial navigation with theta phase coding</a></div><div class="wp-workCard_item"><span>Neurocomputing</span><span>, 2006</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">It is an open question whether the hippocampal cognitive map is useful for navigation with ambigu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">It is an open question whether the hippocampal cognitive map is useful for navigation with ambiguity in space representation. The hippocampus might need to assign different contexts to cell populations in the same place, according to the traditional rate coding scheme. Recent experimental observations on theta phase precession, the theta rhythm-dependent activity in the hippocampus, demonstrate that behavioral sequences are represented in a compressed form in the theta cycle, suggesting the possibility of phase coding of context-dependent information in the hippocampus.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a641e091b8557fd32046d5d830d51f04" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":48307137,"asset_id":7871587,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/48307137/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871587"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871587"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871587; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871587]").text(description); $(".js-view-count[data-work-id=7871587]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871587; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871587']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871586"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/7871586/Hippocampal_theta_phase_coding_for_instantaneous_acquisition_of_experienced_events"><img alt="Research paper thumbnail of Hippocampal theta phase coding for instantaneous acquisition of experienced events" class="work-thumbnail" src="https://attachments.academia-assets.com/48307196/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/7871586/Hippocampal_theta_phase_coding_for_instantaneous_acquisition_of_experienced_events">Hippocampal theta phase coding for instantaneous acquisition of experienced events</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Theta rhythm dependent activity of rat hippocampal cells "theta phase precession" was elucidated ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Theta rhythm dependent activity of rat hippocampal cells "theta phase precession" was elucidated based on the hypothesis that theta phase coding enables instantaneous acquisition of experienced events. By using a neural network model of theta phase coding we demonstrate high acquisition abilities of spatial and temporal events. A theoretical prerequisite for this computational power predicts a hippocampal-entorhinal network mechanism to regulate theta phase coding.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ff9b2a7a626408f2e9f22945bfc84439" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":48307196,"asset_id":7871586,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/48307196/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871586"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871586"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871586; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ff9b2a7a626408f2e9f22945bfc84439" } } $('.js-work-strip[data-work-id=7871586]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7871586,"title":"Hippocampal theta phase coding for instantaneous acquisition of experienced events","internal_url":"https://www.academia.edu/7871586/Hippocampal_theta_phase_coding_for_instantaneous_acquisition_of_experienced_events","owner_id":14698680,"coauthors_can_edit":true,"owner":{"id":14698680,"first_name":"Hiroaki","middle_initials":null,"last_name":"Wagatsuma","page_name":"HiroakiWagatsuma","domain_name":"facebook","created_at":"2014-08-04T13:16:19.153-07:00","display_name":"Hiroaki Wagatsuma","url":"https://facebook.academia.edu/HiroakiWagatsuma"},"attachments":[{"id":48307196,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/48307196/thumbnails/1.jpg","file_name":"IJCNN.2004.138115720160825-19511-1v5e5ku.pdf","download_url":"https://www.academia.edu/attachments/48307196/download_file","bulk_download_file_name":"Hippocampal_theta_phase_coding_for_insta.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/48307196/IJCNN.2004.138115720160825-19511-1v5e5ku-libre.pdf?1472132438=\u0026response-content-disposition=attachment%3B+filename%3DHippocampal_theta_phase_coding_for_insta.pdf\u0026Expires=1740000684\u0026Signature=f-ofbA9gWX6r4zYIj0xd19h-UyJFb7mzQZyy-mWXLFU1jw1jVe8rqf2iYCHPxOXyIdLVXHGMy3PBKDGxQ-HBE2nzG6LHISCKFINS7tWwfWlRKYv44rde8rt97quGLMWucroX2L8A0jwQQUSfD8~PudGZr3lpiQKHTaObqT9aSkv4LLX4HdTAsAuCS92HZTZEc7GyYTMTA8XNIOgFtnlHix9L0zVygYW1OTCUnH0XzTOIZBBqTbbQDi9wNbj7nBm2iPbjTHWn8nwVizFMgCcMuHLOUuPiQ73fL3pAoBEnFz4wevrLog7mUgDr42Z4vj4MW9uKJQ5C9rRI4QEE9~oA0Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871585"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/7871585/Cognitive_Map_Formation_Through_Sequence_Encoding_by_Theta_Phase_Precession"><img alt="Research paper thumbnail of Cognitive Map Formation Through Sequence Encoding by Theta Phase Precession" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/7871585/Cognitive_Map_Formation_Through_Sequence_Encoding_by_Theta_Phase_Precession">Cognitive Map Formation Through Sequence Encoding by Theta Phase Precession</a></div><div class="wp-workCard_item"><span>Neural Computation</span><span>, 2004</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The rodent hippocampus has been thought to represent the spatial environment as a cognitive map. ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The rodent hippocampus has been thought to represent the spatial environment as a cognitive map. The associative connections in the hippocampus imply that a neural entity represents the map as a geometrical network of hippocampal cells in terms of a chart. According to recent experimental observations, the cells fire successively relative to the theta oscillation of the local field potential, called theta phase precession, when the animal is running. This observation suggests the learning of temporal sequences with asymmetric connections in the hippocampus, but it also gives rather inconsistent implications on the formation of the chart that should consist of symmetric connections for space coding. In this study, we hypothesize that the chart is generated with theta phase coding through the integration of asymmetric connections. Our computer experiments use a hippocampal network model to demonstrate that a geometrical network is formed through running experiences in a few minutes. Asymmetric connections are found to remain and distribute heterogeneously in the network. The obtained network exhibits the spatial localization of activities at each instance as the chart does and their propagation that represents behavioral motions with multidirectional properties. We conclude that theta phase precession and the Hebbian rule with a time delay can provide the neural principles for learning the cognitive map.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871585"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871585"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871585; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871585]").text(description); $(".js-view-count[data-work-id=7871585]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871585; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871585']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=7871585]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7871585,"title":"Cognitive Map Formation Through Sequence Encoding by Theta Phase Precession","internal_url":"https://www.academia.edu/7871585/Cognitive_Map_Formation_Through_Sequence_Encoding_by_Theta_Phase_Precession","owner_id":14698680,"coauthors_can_edit":true,"owner":{"id":14698680,"first_name":"Hiroaki","middle_initials":null,"last_name":"Wagatsuma","page_name":"HiroakiWagatsuma","domain_name":"facebook","created_at":"2014-08-04T13:16:19.153-07:00","display_name":"Hiroaki Wagatsuma","url":"https://facebook.academia.edu/HiroakiWagatsuma"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871584"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/7871584/Working_Memory_Dynamics_in_a_Flip_Flop_Oscillations_Network_Model_with_Milnor_Attractor"><img alt="Research paper thumbnail of Working Memory Dynamics in a Flip-Flop Oscillations Network Model with Milnor Attractor" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/7871584/Working_Memory_Dynamics_in_a_Flip_Flop_Oscillations_Network_Model_with_Milnor_Attractor">Working Memory Dynamics in a Flip-Flop Oscillations Network Model with Milnor Attractor</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A phenomenological model is developed where complex dynamics are the correlate of spatio-temporal...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A phenomenological model is developed where complex dynamics are the correlate of spatio-temporal memories. If resting is not a classical fixed point attractor but a Milnor attractor, multiple oscillations appear in the dynamics of a coupled system. This model can be helpful for describing brain activity in terms of well classified dynamics and for implementing human-like real-time computation.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871584"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871584"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871584; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871584]").text(description); $(".js-view-count[data-work-id=7871584]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871584; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871584']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=7871584]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7871584,"title":"Working Memory Dynamics in a Flip-Flop Oscillations Network Model with Milnor Attractor","internal_url":"https://www.academia.edu/7871584/Working_Memory_Dynamics_in_a_Flip_Flop_Oscillations_Network_Model_with_Milnor_Attractor","owner_id":14698680,"coauthors_can_edit":true,"owner":{"id":14698680,"first_name":"Hiroaki","middle_initials":null,"last_name":"Wagatsuma","page_name":"HiroakiWagatsuma","domain_name":"facebook","created_at":"2014-08-04T13:16:19.153-07:00","display_name":"Hiroaki Wagatsuma","url":"https://facebook.academia.edu/HiroakiWagatsuma"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871583"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/7871583/A_Computational_Model_of_Formation_of_Grid_Field_and_Theta_Phase_Precession_in_the_Entorhinal_Cells"><img alt="Research paper thumbnail of A Computational Model of Formation of Grid Field and Theta Phase Precession in the Entorhinal Cells" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/7871583/A_Computational_Model_of_Formation_of_Grid_Field_and_Theta_Phase_Precession_in_the_Entorhinal_Cells">A Computational Model of Formation of Grid Field and Theta Phase Precession in the Entorhinal Cells</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper proposes a computational model of spatio-temporal property formation in the entorhinal...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper proposes a computational model of spatio-temporal property formation in the entorhinal neurons recently known as “grid cells”. The model consists of module structures for local path integration, multiple sensory integration and for theta phase coding of grid fields. Theta phase precession naturally encodes the spatial information in theta phase. The proposed module structures have good agreement with head direction cells and grid cells in the entorhinal cortex. The functional role of theta phase coding in the entorhinal cortex for cognitive map formation in the hippocampus is discussed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871583"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871583"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871583; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871583]").text(description); $(".js-view-count[data-work-id=7871583]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871583; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871583']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=7871583]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7871583,"title":"A Computational Model of Formation of Grid Field and Theta Phase Precession in the Entorhinal Cells","internal_url":"https://www.academia.edu/7871583/A_Computational_Model_of_Formation_of_Grid_Field_and_Theta_Phase_Precession_in_the_Entorhinal_Cells","owner_id":14698680,"coauthors_can_edit":true,"owner":{"id":14698680,"first_name":"Hiroaki","middle_initials":null,"last_name":"Wagatsuma","page_name":"HiroakiWagatsuma","domain_name":"facebook","created_at":"2014-08-04T13:16:19.153-07:00","display_name":"Hiroaki Wagatsuma","url":"https://facebook.academia.edu/HiroakiWagatsuma"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871582"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/7871582/Context_Dependent_Adaptive_Behavior_Generated_in_the_Theta_Phase_Coding_Network"><img alt="Research paper thumbnail of Context-Dependent Adaptive Behavior Generated in the Theta Phase Coding Network" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/7871582/Context_Dependent_Adaptive_Behavior_Generated_in_the_Theta_Phase_Coding_Network">Context-Dependent Adaptive Behavior Generated in the Theta Phase Coding Network</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The real world changes in space over time. Our brains need real-time interaction with the externa...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The real world changes in space over time. Our brains need real-time interaction with the external world and will update various internal representations even when events happen only one-time. Such one-time experiences are evaluated in relation to what happens for us in joy and sorrow. Recent brain studies suggest that the dynamic coordination of different representations in brain areas is governed by the synchronization of the brain oscillation, such as theta rhythms. In the rodent hippocampus, the temporal coding mechanism with the theta rhythm, theta phase coding, provides the ability to encode and retrieve behavioral sequences even in the one-time experience, by using successive firing phases in every theta cycle. We here extended the theory to the large-scale brain network and hypothesized that the phase coding not only represents the current behavioral context, but also properly associates it with the evaluation of what happened in the external environment. It is necessary for the animal to predict events in the near future and to update the current and next executive action. In a maze task on our robotic platform, the acquisition of spatial-temporal sequences and spatial-reward associations were demonstrated, even in few trials, and the association contributes to the current action selection. This result suggests that theta rhythms may contribute to coordinate different neural activities to enable contextual decision-making in the real environment.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871582"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871582"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871582; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871582]").text(description); $(".js-view-count[data-work-id=7871582]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871582; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871582']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=7871582]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7871582,"title":"Context-Dependent Adaptive Behavior Generated in the Theta Phase Coding Network","internal_url":"https://www.academia.edu/7871582/Context_Dependent_Adaptive_Behavior_Generated_in_the_Theta_Phase_Coding_Network","owner_id":14698680,"coauthors_can_edit":true,"owner":{"id":14698680,"first_name":"Hiroaki","middle_initials":null,"last_name":"Wagatsuma","page_name":"HiroakiWagatsuma","domain_name":"facebook","created_at":"2014-08-04T13:16:19.153-07:00","display_name":"Hiroaki Wagatsuma","url":"https://facebook.academia.edu/HiroakiWagatsuma"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="1678505" id="papers"><div class="js-work-strip profile--work_container" data-work-id="7871606"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/7871606/Problems_of_temporal_granularity_in_robot_control_Levels_of_adaptation_and_a_necessity_of_self_confidence"><img alt="Research paper thumbnail of Problems of temporal granularity in robot control: Levels of adaptation and a necessity of self-confidence" class="work-thumbnail" src="https://attachments.academia-assets.com/48307122/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/7871606/Problems_of_temporal_granularity_in_robot_control_Levels_of_adaptation_and_a_necessity_of_self_confidence">Problems of temporal granularity in robot control: Levels of adaptation and a necessity of self-confidence</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The granularity of "action" within a system is highly depending on the internal representation fo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The granularity of "action" within a system is highly depending on the internal representation for the task, or intention of what to do if it is a biological system. In the same time, there are several levels of adaptation when the system tries to complete a mission. The problem of choosing the right level of action representation is essential for robot controls a s well as in learning paradigms. Both tend to use lowgranularity and transfer the processed information to upper levels constructively. However the system never guarantees the completion time of the mission if the system is composed of stiff functional blocks with a specific temporal granularity at the bottom level. However, we biological system have an ability to manage the global time for scheduling and reorganization of tasks to finish by the deadline. Brain-inspired robotics allows us to investigate a distributed parallel information system, the brain, with the ability of time management as a real time control system of the physical body through flexible planning of necessary actions by interacting with the real environment. It is an extension of subsumption approaches that fixed a set of behaviors as the basic unit of action in the viewpoint of temporal property. By focusing on the temporal granularity a s a consequence of coordination among multiple levels, a selfconfident robot control may arise from a coupling between top-down or purpose-oriented decomposition of the purpose to primitive functions with flexible time windows and bottom-up of sensori-motor reactions in dynamic environments.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4c162aa7f0cbba1742c727aa4932fcdf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":48307122,"asset_id":7871606,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/48307122/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871606"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871606"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871606; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871606]").text(description); $(".js-view-count[data-work-id=7871606]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871606; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871606']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871602"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/7871602/Cognitive_robotics_new_insights_into_robot_and_human_intelligence_by_reverse_engineering_brain_functions_From_the_Guest_Editors"><img alt="Research paper thumbnail of Cognitive robotics: new insights into robot and human intelligence by reverse engineering brain functions [From the Guest Editors" class="work-thumbnail" src="https://attachments.academia-assets.com/48307311/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/7871602/Cognitive_robotics_new_insights_into_robot_and_human_intelligence_by_reverse_engineering_brain_functions_From_the_Guest_Editors">Cognitive robotics: new insights into robot and human intelligence by reverse engineering brain functions [From the Guest Editors</a></div><div class="wp-workCard_item"><span>IEEE Robotics & Automation Magazine</span><span>, 2009</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4267f2a39e125944ed72926af38c2489" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":48307311,"asset_id":7871602,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/48307311/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871602"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871602"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871602; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871602]").text(description); $(".js-view-count[data-work-id=7871602]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871602; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871602']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871600"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/7871600/Hybrid_Design_Principles_and_Time_Constants_in_the_Construction_of_Brain_Based_Robotics_A_Real_Time_Simulator_of_Oscillatory_Neural_Networks_Interacting_with_the_Real_Environment_via_Robotic_Devices"><img alt="Research paper thumbnail of Hybrid Design Principles and Time Constants in the Construction of Brain-Based Robotics: A Real-Time Simulator of Oscillatory Neural Networks Interacting with the Real Environment via Robotic Devices" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/7871600/Hybrid_Design_Principles_and_Time_Constants_in_the_Construction_of_Brain_Based_Robotics_A_Real_Time_Simulator_of_Oscillatory_Neural_Networks_Interacting_with_the_Real_Environment_via_Robotic_Devices">Hybrid Design Principles and Time Constants in the Construction of Brain-Based Robotics: A Real-Time Simulator of Oscillatory Neural Networks Interacting with the Real Environment via Robotic Devices</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One of most important concepts in robotics and artificial intelligence is the embodied approach, ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">One of most important concepts in robotics and artificial intelligence is the embodied approach, focusing on the importance of having a body that functionally connects to the external world. This setup suggests that the intelligence develops through sensorimotor skills and through situations that would actually be confronted in the environment. We support this concept and propose to further extend it to embodiment in the time domain. Nervous systems have variable processing times. The different time courses proceed in the nervous system in parallel, and individual circuits independently and cooperatively work under the constraints of temporal properties. We here propose an experimental platform of oscillatory neural networks having real-time communication with the environment through the robot’s body. The synchronization mechanism of oscillations in neural activities have the advantage of synthetic controls known in motor coordination, but we extend this to circuits for cognitive functions like episodic memory formation and decision making of the robotic behavior by using the theta phase coding mechanism. A slow oscillation, like the theta rhythm, enables behavioral temporal sequences to be compressed in sequential firings during each oscillation cycle, and this helps to represent cognitive information in episodes composed of past-present-future structures. The temporal structure is crucial for recognition of the current context and adaptability in dynamic environments, and it smoothly controls sensorimotor local circuits with faster time scales. This work represents a tiny step towards constructing the brain by focusing on the temporal structure, yet this approach may elucidate the new nature of the brain-based intelligence.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871600"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871600"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871600; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871600]").text(description); $(".js-view-count[data-work-id=7871600]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871600; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871600']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=7871600]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7871600,"title":"Hybrid Design Principles and Time Constants in the Construction of Brain-Based Robotics: A Real-Time Simulator of Oscillatory Neural Networks Interacting with the Real Environment via Robotic Devices","internal_url":"https://www.academia.edu/7871600/Hybrid_Design_Principles_and_Time_Constants_in_the_Construction_of_Brain_Based_Robotics_A_Real_Time_Simulator_of_Oscillatory_Neural_Networks_Interacting_with_the_Real_Environment_via_Robotic_Devices","owner_id":14698680,"coauthors_can_edit":true,"owner":{"id":14698680,"first_name":"Hiroaki","middle_initials":null,"last_name":"Wagatsuma","page_name":"HiroakiWagatsuma","domain_name":"facebook","created_at":"2014-08-04T13:16:19.153-07:00","display_name":"Hiroaki Wagatsuma","url":"https://facebook.academia.edu/HiroakiWagatsuma"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871599"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/7871599/Cultivated_Microorganisms_Control_a_Real_Robot_A_Model_of_Dynamical_Coupling_between_Internal_Growth_and_Robot_Movement"><img alt="Research paper thumbnail of Cultivated Microorganisms Control a Real Robot: A Model of Dynamical Coupling between Internal Growth and Robot Movement" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/7871599/Cultivated_Microorganisms_Control_a_Real_Robot_A_Model_of_Dynamical_Coupling_between_Internal_Growth_and_Robot_Movement">Cultivated Microorganisms Control a Real Robot: A Model of Dynamical Coupling between Internal Growth and Robot Movement</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In biological systems, internal microorganisms adaptively survive but often serve necessary funct...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In biological systems, internal microorganisms adaptively survive but often serve necessary functions to the host, such as energy production by mitochondria as symbiosis. On the other hand, malignant germs have a parasitic relationship with the host and provide no benefit. A significant property that distinguishes healthy symbioses and malignant parasites is reproduction speed, or pace. For example, the rapid reproduction of influenza viruses destructs the host system, resulting in death. This study explored the necessary temporal property to establish a healthy relationship with the host under conditions where internal organisms have individual life spans. We propose a simple model of microorganisms, which are distributed spatially as colonial organizations undergoing temporal evolution and hypothesize that a self-consistent rhythm generated in collective behavior that is functionally coupled with the temporal global property of the host system is critical. To investigate the real-time coordination capability, an experimental framework with a mobile robot moving in the real world was used. As the on-line system, the microorganism model controls this robot. In this model, microorganisms expanded spatially and had colonial and power law distributions through time evolution. The neighboring distances, which are crucial for reproduction speed and are globally modulated by the size of the whole living area, are plastically changed to exhibit a rhythmic modulation. In the real-environmental experiment, the robot’s navigation was successfully demonstrated by producing a temporal adaptability of microorganisms with the living area reshaped according to the current sensory information of the mobile robot. This is a first step of the microorganism-based framework to investigate the real-time coordination mechanism between internal and external timescales. The result may further groundbreaking research of bio-morphological robots.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871599"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871599"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871599; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871599]").text(description); $(".js-view-count[data-work-id=7871599]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871599; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871599']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=7871599]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7871599,"title":"Cultivated Microorganisms Control a Real Robot: A Model of Dynamical Coupling between Internal Growth and Robot Movement","internal_url":"https://www.academia.edu/7871599/Cultivated_Microorganisms_Control_a_Real_Robot_A_Model_of_Dynamical_Coupling_between_Internal_Growth_and_Robot_Movement","owner_id":14698680,"coauthors_can_edit":true,"owner":{"id":14698680,"first_name":"Hiroaki","middle_initials":null,"last_name":"Wagatsuma","page_name":"HiroakiWagatsuma","domain_name":"facebook","created_at":"2014-08-04T13:16:19.153-07:00","display_name":"Hiroaki Wagatsuma","url":"https://facebook.academia.edu/HiroakiWagatsuma"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871594"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/7871594/Computational_study_of_memory_formation_through_dynamical_interplays_in_the_cortico_hippocampal_system"><img alt="Research paper thumbnail of Computational study of memory formation through dynamical interplays in the cortico-hippocampal system" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/7871594/Computational_study_of_memory_formation_through_dynamical_interplays_in_the_cortico_hippocampal_system">Computational study of memory formation through dynamical interplays in the cortico-hippocampal system</a></div><div class="wp-workCard_item"><span>Neuroscience Research</span><span>, 2009</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871594"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871594"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871594; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871594]").text(description); $(".js-view-count[data-work-id=7871594]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871594; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871594']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=7871594]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7871594,"title":"Computational study of memory formation through dynamical interplays in the cortico-hippocampal system","internal_url":"https://www.academia.edu/7871594/Computational_study_of_memory_formation_through_dynamical_interplays_in_the_cortico_hippocampal_system","owner_id":14698680,"coauthors_can_edit":true,"owner":{"id":14698680,"first_name":"Hiroaki","middle_initials":null,"last_name":"Wagatsuma","page_name":"HiroakiWagatsuma","domain_name":"facebook","created_at":"2014-08-04T13:16:19.153-07:00","display_name":"Hiroaki Wagatsuma","url":"https://facebook.academia.edu/HiroakiWagatsuma"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871593"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/7871593/A_unified_view_of_theta_phase_coding_in_the_entorhinal_hippocampal_system"><img alt="Research paper thumbnail of A unified view of theta-phase coding in the entorhinal–hippocampal system" class="work-thumbnail" src="https://attachments.academia-assets.com/48307149/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/7871593/A_unified_view_of_theta_phase_coding_in_the_entorhinal_hippocampal_system">A unified view of theta-phase coding in the entorhinal–hippocampal system</a></div><div class="wp-workCard_item"><span>Current Opinion in Neurobiology</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The discovery of theta-rhythm-dependent firing of rodent hippocampal neurons highlighted the func...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The discovery of theta-rhythm-dependent firing of rodent hippocampal neurons highlighted the functional significance of temporal encoding in hippocampal memory. However, earlier theoretical studies on this topic seem divergent and experimental implications are invariably complicated. To obtain a unified understanding of neural dynamics in the hippocampal memory, we here review recent developments in computational models and experimental discoveries on the 'theta-phase precession' of hippocampal place cells and entorhinal grid cells. We identify a theoretical hypothesis that is well supported by experimental facts; this model reveals a significant contribution of theta-phase coding to the on-line real-time operation of episodic events, through highly parallel representation of spatiotemporal information.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e8ce3af864477631e2024c99c55efe60" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":48307149,"asset_id":7871593,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/48307149/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871593"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871593"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871593; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871592"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/7871592/Dynamic_Brain_Platform"><img alt="Research paper thumbnail of Dynamic Brain Platform" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/7871592/Dynamic_Brain_Platform">Dynamic Brain Platform</a></div><div class="wp-workCard_item"><span>Neuroscience Research</span><span>, 2009</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871592"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871592"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871592; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871592]").text(description); $(".js-view-count[data-work-id=7871592]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871592; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871592']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=7871592]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7871592,"title":"Dynamic Brain Platform","internal_url":"https://www.academia.edu/7871592/Dynamic_Brain_Platform","owner_id":14698680,"coauthors_can_edit":true,"owner":{"id":14698680,"first_name":"Hiroaki","middle_initials":null,"last_name":"Wagatsuma","page_name":"HiroakiWagatsuma","domain_name":"facebook","created_at":"2014-08-04T13:16:19.153-07:00","display_name":"Hiroaki Wagatsuma","url":"https://facebook.academia.edu/HiroakiWagatsuma"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871591"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/7871591/Synchronized_Rhythmic_Signals_Effectively_Influence_Ongoing_Cortical_Activity_for_Decision_Making_A_Study_of_the_Biological_Plausible_Neural_Network_Model"><img alt="Research paper thumbnail of Synchronized Rhythmic Signals Effectively Influence Ongoing Cortical Activity for Decision-Making: A Study of the Biological Plausible Neural Network Model" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/7871591/Synchronized_Rhythmic_Signals_Effectively_Influence_Ongoing_Cortical_Activity_for_Decision_Making_A_Study_of_the_Biological_Plausible_Neural_Network_Model">Synchronized Rhythmic Signals Effectively Influence Ongoing Cortical Activity for Decision-Making: A Study of the Biological Plausible Neural Network Model</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The brain is capable of parallel processing of different types of information in different brain ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The brain is capable of parallel processing of different types of information in different brain regions. However, for higher cognitive functions such as decision-making, such regionally distributed information must interact together in the proper timing. The prefrontal cortex, the region which carries out decision-making, needs to be modulated by external signals to represent the current behavioral context in the hippocampus (HP). The question remains as to how the firing activity of the cortical neural network can be modulated by external signals in corporation with the ongoing activity. We hypothesized that rhythmic signals that attempt to synchronize the cortical ongoing activity minimize the disturbance and effectively enhance the activities of selective neurons. We investigated the level of the modulation by using a mutually connected neural network that consists of a neuron model with excitatory and refractory periods. The results demonstrated that cortical ongoing activities are weakly modulated by random external signals, while synchronized rhythmic signals, given as the pseudo HP signals, selectively enhance cortical activities. This suggests that the cortical ongoing activity is effectively influenced by the synchronized signals, which carry information in the proper timing of excitation. The investigation of neural synchronization dynamics is important to understanding how the brain realizes parallel processing in different sub-regions and to update immediately the internal representation even if the previous internal processing is ongoing.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871591"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871591"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871591; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871591]").text(description); $(".js-view-count[data-work-id=7871591]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871591; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871591']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=7871591]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7871591,"title":"Synchronized Rhythmic Signals Effectively Influence Ongoing Cortical Activity for Decision-Making: A Study of the Biological Plausible Neural Network Model","internal_url":"https://www.academia.edu/7871591/Synchronized_Rhythmic_Signals_Effectively_Influence_Ongoing_Cortical_Activity_for_Decision_Making_A_Study_of_the_Biological_Plausible_Neural_Network_Model","owner_id":14698680,"coauthors_can_edit":true,"owner":{"id":14698680,"first_name":"Hiroaki","middle_initials":null,"last_name":"Wagatsuma","page_name":"HiroakiWagatsuma","domain_name":"facebook","created_at":"2014-08-04T13:16:19.153-07:00","display_name":"Hiroaki Wagatsuma","url":"https://facebook.academia.edu/HiroakiWagatsuma"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871590"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/7871590/A_neural_network_model_self_organizing_a_cognitive_map_using_theta_phase_precession"><img alt="Research paper thumbnail of A neural network model self-organizing a cognitive map using theta phase precession" class="work-thumbnail" src="https://attachments.academia-assets.com/48307109/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/7871590/A_neural_network_model_self_organizing_a_cognitive_map_using_theta_phase_precession">A neural network model self-organizing a cognitive map using theta phase precession</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The cognitive map is thought to consist of place cells in the hippocampus. We hypothesize that “t...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The cognitive map is thought to consist of place cells in the hippocampus. We hypothesize that “theta phase precession”, a characteristic relation between the firing of the place cell and local field theta rhythm, contributes to the generation of the cognitive map as a network of place cells. We propose a network model of the hippocampus based on the hypothesis. In our model, the entorhinal cortex (EC) receives sensory-inputs of local views. The dynamic pattern of theta phase precession is generated by mutual synchronization of neural oscillators in the EC. In CA3, neural units receive inputs from the EC and the cognitive map is self-organized in the recurrent neural network. In the computer experiments, inheritance of theta phase precession in CA3 results in a kind of phase wave propagating along a 2-D plane. The 2-D plane is define as the desired array of place cells in accordance with their place fields. The wave propagation deriving from theta phase precession globally controls the synaptic modification among place cells so that their network is self-organized as the cognitive map</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f9c62de428f50ae6f2fb5d202a97a59e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":48307109,"asset_id":7871590,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/48307109/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871590"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871590"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871590; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871589"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/7871589/Design_of_Web_Agents_Inspired_by_Brain_Research"><img alt="Research paper thumbnail of Design of Web Agents Inspired by Brain Research" class="work-thumbnail" src="https://attachments.academia-assets.com/34361809/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/7871589/Design_of_Web_Agents_Inspired_by_Brain_Research">Design of Web Agents Inspired by Brain Research</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The paper presents an approach to combine knowledge from memory and brain sciences with informati...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The paper presents an approach to combine knowledge from memory and brain sciences with information retrieval research in the design of Web agents. An information retrieval agent for classification of Web pages based on genre features is used. In developing the agent to adapt to users' search preferences, a neuro-cognitive model of human episodic memory is employed. Our studies show that neuro-realistic models, capable of abstraction of meaningful fragments of knowledge, rather than snapshots of the retrieved Web pages, are closer to the human way of interacting with the Web and can be used for optimization of agent performance.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c83c9fdbee995c6294e5fa663b112924" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":34361809,"asset_id":7871589,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/34361809/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871589"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871589"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871589; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "c83c9fdbee995c6294e5fa663b112924" } } $('.js-work-strip[data-work-id=7871589]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7871589,"title":"Design of Web Agents Inspired by Brain Research","internal_url":"https://www.academia.edu/7871589/Design_of_Web_Agents_Inspired_by_Brain_Research","owner_id":14698680,"coauthors_can_edit":true,"owner":{"id":14698680,"first_name":"Hiroaki","middle_initials":null,"last_name":"Wagatsuma","page_name":"HiroakiWagatsuma","domain_name":"facebook","created_at":"2014-08-04T13:16:19.153-07:00","display_name":"Hiroaki Wagatsuma","url":"https://facebook.academia.edu/HiroakiWagatsuma"},"attachments":[{"id":34361809,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/34361809/thumbnails/1.jpg","file_name":"paper02.pdf","download_url":"https://www.academia.edu/attachments/34361809/download_file","bulk_download_file_name":"Design_of_Web_Agents_Inspired_by_Brain_R.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/34361809/paper02-libre.pdf?1407183895=\u0026response-content-disposition=attachment%3B+filename%3DDesign_of_Web_Agents_Inspired_by_Brain_R.pdf\u0026Expires=1740000684\u0026Signature=bdRmldG0x42In-e04dXCrAQ4bOWkY8iGyCfPvlWTGtwXAMeKZ-Aq-PN~DV639NLAm7qq~eNNXvwUybcXVv8h2bYUXkgD949pLtHFs9uRzx-RCu0ErRsaS8ByRq18CEEKoR6r6n~DoA-ted-T9HQdJJBsV8kBh6bWcH8bFAsnMc8~JKbxdILypV~eFSGabFH5hP2PtpkQtgKgrxySytiAtVVMhajufGJeNSgD9bHHOzzE~-zxnZltv95wcuHE-z69tB261L1WcJiBoKK4A6iTSdZ7xP3jR7JhhwBQrIxFKABTmtL7AI7Z3auPzapN~aSkJg6A3GH7T0taZryYUY-pSw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":34361810,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/34361810/thumbnails/1.jpg","file_name":"paper02.pdf","download_url":"https://www.academia.edu/attachments/34361810/download_file","bulk_download_file_name":"Design_of_Web_Agents_Inspired_by_Brain_R.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/34361810/paper02-libre.pdf?1407183896=\u0026response-content-disposition=attachment%3B+filename%3DDesign_of_Web_Agents_Inspired_by_Brain_R.pdf\u0026Expires=1740000684\u0026Signature=Zd1-6lB6B1QgI5TgQdfvGmCCcBzj-1aKhGOPU18EjZvYMcwzRZCU6D58kGAdKNNz9SZIQ7kvNkmBsr4FqAtSGlZSB-2ipcZYrM7wZn6TbxcgtXglvdWsetvu9UTykhhgp~Y2vsoMPgYiYv5gVw2OBksF9s7uATY69ccB60xXAFxqltB363O2BatXRggmwXqGZMHNKH08tkLwFEcFb1rCv-ChQs0WcjHISdfagrcWSTCAA76jvlRwLwnW~CqM8zKJGfuo9K5Oi-ulEkBncNTRtM1PY3N6oTPhuL~eceVGDUKUosW8TMDQSv2L86gTaIO62bkBWhjCnkt~P3L5MoLvIw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871588"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/7871588/Neural_dynamics_of_the_cognitive_map_in_the_hippocampus"><img alt="Research paper thumbnail of Neural dynamics of the cognitive map in the hippocampus" class="work-thumbnail" src="https://attachments.academia-assets.com/48307113/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/7871588/Neural_dynamics_of_the_cognitive_map_in_the_hippocampus">Neural dynamics of the cognitive map in the hippocampus</a></div><div class="wp-workCard_item"><span>Cognitive Neurodynamics</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The rodent hippocampus has been thought to represent the spatial environment as a cognitive map. ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The rodent hippocampus has been thought to represent the spatial environment as a cognitive map. In the classical theory, the cognitive map has been explained as a consequence of the fact that different spatial regions are assigned to different cell populations in the framework of rate coding. Recently, the relation between place cell firing and local field oscillation theta in terms of theta phase precession was experimentally discovered and suggested as a temporal coding mechanism leading to memory formation of behavioral sequences accompanied with asymmetric Hebbian plasticity. The cognitive map theory is apparently outside of the sequence memory view. Therefore, theoretical analysis is necessary to consider the biological neural dynamics for the sequence encoding of the memory of behavioral sequences, providing the cognitive map formation. In this article, we summarize the theoretical neural dynamics of the real-time sequence encoding by theta phase precession, called theta phase coding, and review a series of theoretical models with the theta phase coding that we previously reported. With respect to memory encoding functions, instantaneous memory formation of one-time experience was first demonstrated, and then the ability of integration of memories of behavioral sequences into a network of the cognitive map was shown. In terms of memory retrieval functions, theta phase coding enables the hippocampus to represent the spatial location in the current behavioral context even with ambiguous sensory input when multiple sequences were coded. Finally, for utilization, retrieved temporal sequences in the hippocampus can be available for action selection, through the process of reverting theta rhythm-dependent activities to information in the behavioral time scale. This theoretical approach allows us to investigate how the behavioral sequences are encoded, updated, retrieved and used in the hippocampus, as the real-time interaction with the external environment. It may indeed be the bridge to the episodic memory function in human hippocampus.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cac93cf507365276718b703057aabf16" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":48307113,"asset_id":7871588,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/48307113/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871588"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871588"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871588; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "cac93cf507365276718b703057aabf16" } } $('.js-work-strip[data-work-id=7871588]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7871588,"title":"Neural dynamics of the cognitive map in the hippocampus","internal_url":"https://www.academia.edu/7871588/Neural_dynamics_of_the_cognitive_map_in_the_hippocampus","owner_id":14698680,"coauthors_can_edit":true,"owner":{"id":14698680,"first_name":"Hiroaki","middle_initials":null,"last_name":"Wagatsuma","page_name":"HiroakiWagatsuma","domain_name":"facebook","created_at":"2014-08-04T13:16:19.153-07:00","display_name":"Hiroaki Wagatsuma","url":"https://facebook.academia.edu/HiroakiWagatsuma"},"attachments":[{"id":48307113,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/48307113/thumbnails/1.jpg","file_name":"Neural_dynamics_of_the_cognitive_map_in_20160825-23156-3kfjp5.pdf","download_url":"https://www.academia.edu/attachments/48307113/download_file","bulk_download_file_name":"Neural_dynamics_of_the_cognitive_map_in.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/48307113/Neural_dynamics_of_the_cognitive_map_in_20160825-23156-3kfjp5-libre.pdf?1472132442=\u0026response-content-disposition=attachment%3B+filename%3DNeural_dynamics_of_the_cognitive_map_in.pdf\u0026Expires=1740000684\u0026Signature=VXufOEe~mjU3knn3ZKmuXTz~QBpnaRzmCYBp54SxOMSUMBK~nWUx1RxN2nGCamU4Fs7hGMte8QdAQZRJXSNlVUCUbwayo5KXw87RRar2LcemhORG7OflX1WV~V6ckV1WuSceD47biHcmTs7QeQqQcdCqP~QkIF5hLFnvP6WEgXGXijA8xRk~BqRzBbp4liz85XsVrzyfJAm9YE~wIKIDlh9y8edTwz~216xikBL9llLrsH7uHdEzD-myxwnBvMpemP00d9GnFaI48Hx3msRPsN~lFhB~JGq8agakam25vDJhwMsFF4vO25nFvxFjHfD36ABgaY092gIz1g5UNYwgsg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871587"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/7871587/Disambiguation_in_spatial_navigation_with_theta_phase_coding"><img alt="Research paper thumbnail of Disambiguation in spatial navigation with theta phase coding" class="work-thumbnail" src="https://attachments.academia-assets.com/48307137/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/7871587/Disambiguation_in_spatial_navigation_with_theta_phase_coding">Disambiguation in spatial navigation with theta phase coding</a></div><div class="wp-workCard_item"><span>Neurocomputing</span><span>, 2006</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">It is an open question whether the hippocampal cognitive map is useful for navigation with ambigu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">It is an open question whether the hippocampal cognitive map is useful for navigation with ambiguity in space representation. The hippocampus might need to assign different contexts to cell populations in the same place, according to the traditional rate coding scheme. Recent experimental observations on theta phase precession, the theta rhythm-dependent activity in the hippocampus, demonstrate that behavioral sequences are represented in a compressed form in the theta cycle, suggesting the possibility of phase coding of context-dependent information in the hippocampus.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a641e091b8557fd32046d5d830d51f04" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":48307137,"asset_id":7871587,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/48307137/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871587"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871587"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871587; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871586"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/7871586/Hippocampal_theta_phase_coding_for_instantaneous_acquisition_of_experienced_events"><img alt="Research paper thumbnail of Hippocampal theta phase coding for instantaneous acquisition of experienced events" class="work-thumbnail" src="https://attachments.academia-assets.com/48307196/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/7871586/Hippocampal_theta_phase_coding_for_instantaneous_acquisition_of_experienced_events">Hippocampal theta phase coding for instantaneous acquisition of experienced events</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Theta rhythm dependent activity of rat hippocampal cells "theta phase precession" was elucidated ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Theta rhythm dependent activity of rat hippocampal cells "theta phase precession" was elucidated based on the hypothesis that theta phase coding enables instantaneous acquisition of experienced events. By using a neural network model of theta phase coding we demonstrate high acquisition abilities of spatial and temporal events. A theoretical prerequisite for this computational power predicts a hippocampal-entorhinal network mechanism to regulate theta phase coding.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ff9b2a7a626408f2e9f22945bfc84439" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":48307196,"asset_id":7871586,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/48307196/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871586"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871586"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871586; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871585"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/7871585/Cognitive_Map_Formation_Through_Sequence_Encoding_by_Theta_Phase_Precession"><img alt="Research paper thumbnail of Cognitive Map Formation Through Sequence Encoding by Theta Phase Precession" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/7871585/Cognitive_Map_Formation_Through_Sequence_Encoding_by_Theta_Phase_Precession">Cognitive Map Formation Through Sequence Encoding by Theta Phase Precession</a></div><div class="wp-workCard_item"><span>Neural Computation</span><span>, 2004</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The rodent hippocampus has been thought to represent the spatial environment as a cognitive map. ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The rodent hippocampus has been thought to represent the spatial environment as a cognitive map. The associative connections in the hippocampus imply that a neural entity represents the map as a geometrical network of hippocampal cells in terms of a chart. According to recent experimental observations, the cells fire successively relative to the theta oscillation of the local field potential, called theta phase precession, when the animal is running. This observation suggests the learning of temporal sequences with asymmetric connections in the hippocampus, but it also gives rather inconsistent implications on the formation of the chart that should consist of symmetric connections for space coding. In this study, we hypothesize that the chart is generated with theta phase coding through the integration of asymmetric connections. Our computer experiments use a hippocampal network model to demonstrate that a geometrical network is formed through running experiences in a few minutes. Asymmetric connections are found to remain and distribute heterogeneously in the network. The obtained network exhibits the spatial localization of activities at each instance as the chart does and their propagation that represents behavioral motions with multidirectional properties. We conclude that theta phase precession and the Hebbian rule with a time delay can provide the neural principles for learning the cognitive map.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871585"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871585"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871585; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871585]").text(description); $(".js-view-count[data-work-id=7871585]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871585; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871585']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=7871585]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7871585,"title":"Cognitive Map Formation Through Sequence Encoding by Theta Phase Precession","internal_url":"https://www.academia.edu/7871585/Cognitive_Map_Formation_Through_Sequence_Encoding_by_Theta_Phase_Precession","owner_id":14698680,"coauthors_can_edit":true,"owner":{"id":14698680,"first_name":"Hiroaki","middle_initials":null,"last_name":"Wagatsuma","page_name":"HiroakiWagatsuma","domain_name":"facebook","created_at":"2014-08-04T13:16:19.153-07:00","display_name":"Hiroaki Wagatsuma","url":"https://facebook.academia.edu/HiroakiWagatsuma"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871584"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/7871584/Working_Memory_Dynamics_in_a_Flip_Flop_Oscillations_Network_Model_with_Milnor_Attractor"><img alt="Research paper thumbnail of Working Memory Dynamics in a Flip-Flop Oscillations Network Model with Milnor Attractor" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/7871584/Working_Memory_Dynamics_in_a_Flip_Flop_Oscillations_Network_Model_with_Milnor_Attractor">Working Memory Dynamics in a Flip-Flop Oscillations Network Model with Milnor Attractor</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A phenomenological model is developed where complex dynamics are the correlate of spatio-temporal...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A phenomenological model is developed where complex dynamics are the correlate of spatio-temporal memories. If resting is not a classical fixed point attractor but a Milnor attractor, multiple oscillations appear in the dynamics of a coupled system. This model can be helpful for describing brain activity in terms of well classified dynamics and for implementing human-like real-time computation.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871584"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871584"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871584; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871584]").text(description); $(".js-view-count[data-work-id=7871584]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871584; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871584']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=7871584]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7871584,"title":"Working Memory Dynamics in a Flip-Flop Oscillations Network Model with Milnor Attractor","internal_url":"https://www.academia.edu/7871584/Working_Memory_Dynamics_in_a_Flip_Flop_Oscillations_Network_Model_with_Milnor_Attractor","owner_id":14698680,"coauthors_can_edit":true,"owner":{"id":14698680,"first_name":"Hiroaki","middle_initials":null,"last_name":"Wagatsuma","page_name":"HiroakiWagatsuma","domain_name":"facebook","created_at":"2014-08-04T13:16:19.153-07:00","display_name":"Hiroaki Wagatsuma","url":"https://facebook.academia.edu/HiroakiWagatsuma"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871583"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/7871583/A_Computational_Model_of_Formation_of_Grid_Field_and_Theta_Phase_Precession_in_the_Entorhinal_Cells"><img alt="Research paper thumbnail of A Computational Model of Formation of Grid Field and Theta Phase Precession in the Entorhinal Cells" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/7871583/A_Computational_Model_of_Formation_of_Grid_Field_and_Theta_Phase_Precession_in_the_Entorhinal_Cells">A Computational Model of Formation of Grid Field and Theta Phase Precession in the Entorhinal Cells</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper proposes a computational model of spatio-temporal property formation in the entorhinal...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper proposes a computational model of spatio-temporal property formation in the entorhinal neurons recently known as “grid cells”. The model consists of module structures for local path integration, multiple sensory integration and for theta phase coding of grid fields. Theta phase precession naturally encodes the spatial information in theta phase. The proposed module structures have good agreement with head direction cells and grid cells in the entorhinal cortex. The functional role of theta phase coding in the entorhinal cortex for cognitive map formation in the hippocampus is discussed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871583"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871583"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871583; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=7871583]").text(description); $(".js-view-count[data-work-id=7871583]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 7871583; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='7871583']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=7871583]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":7871583,"title":"A Computational Model of Formation of Grid Field and Theta Phase Precession in the Entorhinal Cells","internal_url":"https://www.academia.edu/7871583/A_Computational_Model_of_Formation_of_Grid_Field_and_Theta_Phase_Precession_in_the_Entorhinal_Cells","owner_id":14698680,"coauthors_can_edit":true,"owner":{"id":14698680,"first_name":"Hiroaki","middle_initials":null,"last_name":"Wagatsuma","page_name":"HiroakiWagatsuma","domain_name":"facebook","created_at":"2014-08-04T13:16:19.153-07:00","display_name":"Hiroaki Wagatsuma","url":"https://facebook.academia.edu/HiroakiWagatsuma"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="7871582"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/7871582/Context_Dependent_Adaptive_Behavior_Generated_in_the_Theta_Phase_Coding_Network"><img alt="Research paper thumbnail of Context-Dependent Adaptive Behavior Generated in the Theta Phase Coding Network" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/7871582/Context_Dependent_Adaptive_Behavior_Generated_in_the_Theta_Phase_Coding_Network">Context-Dependent Adaptive Behavior Generated in the Theta Phase Coding Network</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The real world changes in space over time. Our brains need real-time interaction with the externa...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The real world changes in space over time. Our brains need real-time interaction with the external world and will update various internal representations even when events happen only one-time. Such one-time experiences are evaluated in relation to what happens for us in joy and sorrow. Recent brain studies suggest that the dynamic coordination of different representations in brain areas is governed by the synchronization of the brain oscillation, such as theta rhythms. In the rodent hippocampus, the temporal coding mechanism with the theta rhythm, theta phase coding, provides the ability to encode and retrieve behavioral sequences even in the one-time experience, by using successive firing phases in every theta cycle. We here extended the theory to the large-scale brain network and hypothesized that the phase coding not only represents the current behavioral context, but also properly associates it with the evaluation of what happened in the external environment. It is necessary for the animal to predict events in the near future and to update the current and next executive action. In a maze task on our robotic platform, the acquisition of spatial-temporal sequences and spatial-reward associations were demonstrated, even in few trials, and the association contributes to the current action selection. This result suggests that theta rhythms may contribute to coordinate different neural activities to enable contextual decision-making in the real environment.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="7871582"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="7871582"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 7871582; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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